Paper Info
Title
Mining Recent Approximate Frequent Items in Wireless Sensor Networks
Abstract
Mining frequent items from sensory data is a major research problem in wireless sensor networks (WSNs) and it can be widely used in environmental monitoring. Conventional lossy counting algorithm can be applied to solve this problem in centralized manner. However, centralized algorithm brings severely data collision in WSNs, and results in inaccurate mining results. In this paper, we present D-FIMA, a distributed frequent items mining algorithm. D-FIMA, running at every sensor node, establishes items aggregation tree via forwarding mining request beforehand, and each node maintains local approximate frequent items. The root of the aggregation tree outputs the final global approximate frequent items. Theoretical analysis and the simulation results show that energy consumption of D-FIMA is much less than the centralized algorithm, and mining results of D-FIMA is more accurate than the centralized algorithm.
Year
DOI
Venue
2009
10.1109/FSKD.2009.607
FSKD (2)
Keywords
Field
DocType
centralized algorithm,wsn,sensory data mining,wireless sensor network,recent approximate frequent items,aggregation tree,environmental monitoring,mining result,data collision,telecommunication computing,local approximate frequent item,items aggregation tree,centralized manner,inaccurate mining result,data mining,wireless sensor networks,frequent items mining algorithm,global approximate frequent item,lossy counting algorithm,forwarding mining request,frequent items,recent approximate frequent item,pediatrics,approximation algorithms,network topology,association rules
Sensor node,Data mining,Approximation algorithm,Lossy compression,GSP Algorithm,Computer science,Network topology,Association rule learning,Wireless sensor network,Energy consumption,Distributed computing
Conference
Volume
ISBN
Citations 
2
978-0-7695-3735-1
3
PageRank 
References 
Authors
0.39
12
2
Name
Order
Citations
PageRank
Meirui Ren1217.30
Longjiang Guo217726.73